Simultaneous muscle water T2 and fat fraction mapping using transverse relaxometry with stimulated echo compensation.

Skeletal muscle inflammation/necrosis and fat infiltration are strong indicators of disease activity and progression in many neuromuscular disorders. They can be assessed by muscle T2 relaxometry and water-fat separation techniques, respectively. In the present work, we exploited differences between water and fat T1 and T2 relaxivities by applying a bi-component extended phase graph (EPG) fitting approach to simultaneously quantify the muscle water T2 and fat fraction from standard multi-slice multi-echo (MSME) acquisitions in the presence of stimulated echoes. Experimental decay curves were adjusted to the theoretical model using either an iterative non-negative least-squares (NNLS) procedure or a pattern recognition approach. Twenty-two patients (age, 49 ± 18 years) were selected to cover a large range of muscle fat infiltration. Four cases of chronic or subchronic juvenile dermatomyositis (age, 8 ± 3 years) were investigated before and 3 months following steroid treatment. For control, five healthy volunteers (age, 25 ± 2 years) were recruited. All subjects underwent the MSME sequence and EPG fitting procedure. The EPG fitting algorithm allowed a precise estimation of water T2 and fat fraction in diseased muscle, even in the presence of large B1(+) inhomogeneities. In the whole cohort of patients, there was no overall correlation between water T2 values obtained with the proposed method and the fat fraction estimated inside muscle tissues (R(2) = 0.02). In the patients with dermatomyositis, there was a significant decrease in water T2 (-4.09 ± 3.7 ms) consequent to steroid treatment. The pattern recognition approach resulted in a 20-fold decrease in processing time relative to the iterative NNLS procedure. The fat fraction derived from the EPG fitting approach correlated well with the fat fraction derived from a standard three-point Dixon method (≈1.5% bias). The bi-component EPG fitting analysis is a precise tool to monitor muscle tissue disease activity and is able to handle bias introduced by fat infiltration and B1(+) inhomogeneities.